Preprints
https://doi.org/10.5194/gmd-2021-301
https://doi.org/10.5194/gmd-2021-301

Submitted as: model description paper 19 Oct 2021

Submitted as: model description paper | 19 Oct 2021

Review status: this preprint is currently under review for the journal GMD.

GSTools v1.3: A toolbox for geostatistical modelling in Python

Sebastian Müller1,2, Lennart Schüler2,1,3, Alraune Zech4,1, and Falk Heße2,1 Sebastian Müller et al.
  • 1Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
  • 2Institute of Earth and Environmental Sciences, University Potsdam, Potsdam, Germany
  • 3Center for Advanced Systems Understanding (CASUS), Görlitz, Germany
  • 4Department of Earth Sciences, University Utrecht, Utrecht, Netherlands

Abstract. Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of e.g. Earth Sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields, it can perform kriging and variogram estimation and much more. We demonstrate its abilities by virtue of a series of example application detailing their use.

Sebastian Müller et al.

Status: open (until 17 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-301', Anonymous Referee #1, 21 Nov 2021 reply

Sebastian Müller et al.

Data sets

Geostatistical Exercises with the Herten Aquifer Lennart Schüler, Sebastian Müller https://doi.org/10.5281/zenodo.5159657

Characterizing Mean Drawdowns of a Pumping Test Ensemble Sebastian Müller, Alraune Zech https://doi.org/10.5281/zenodo.4891874

Regression Kriging vs. Universal Kriging: Finding a North-South Temperature Trend Sebastian Müller https://doi.org/10.5281/zenodo.5159727

Heterogeneous Transport Simulation: The Impact of Connectivity Sebastian Müller, Alraune Zech https://doi.org/10.5281/zenodo.5159577

Model code and software

GSTools Sebastian Müller, Lennart Schüler https://doi.org/10.5281/zenodo.1313628

Sebastian Müller et al.

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Short summary
The GSTools package provides a Python-based platform for gesotatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.